Data Management Intern (EDM) — Hybrid Summer

MFS International Australia Pty Ltd; Boston (MA); Hybrid; USD 25,000 - 36,000; Full time

Job summary

MFS International Australia Pty Ltd is seeking an Enterprise Data Management Intern to support data quality activities and governance for investment data domains. You will work with the EDM group to assist in data analysis, visualization, and metadata management, contributing to data initiatives across the enterprise.

You will gain exposure to datasets and systems such as Bloomberg PORT, Charles River, Eagle, and Snowflake while developing practical skills in data management.

Qualifications

• Effective written and verbal communication skills.
• Strong analytical abilities with data-driven mindset.
• Ability to develop creative solutions to address business needs.
• Ability to work independently and in a team environment.
• Self-motivated with initiative and ownership.
• Experience with Word, Excel, PowerPoint.

Responsibilities

• Strengthen business documentation for core investment data domains.
• Assist Data Analysts with data analysis and visualization.
• Run daily and monthly data quality checks and identify improvement opportunities.
• Contribute to EDM data initiatives and perform data analysis.
• Support Investments and Distribution Triage activities; ensure metadata completeness.
• Maintain the EDM SharePoint site and assist on data-focused projects.

Skills

Communication

Analytical

Problem solving

Teamwork

Initiative

MS Office

Tools

Bloomberg PORT

Charles River

Eagle

Snowflake

Job description

MFS International Australia Pty Ltd is seeking an Enterprise Data Management Intern to support data quality activities and governance for investment data domains. You will work with the EDM group to assist in data analysis, visualization, and metadata management, contributing to data initiatives across the enterprise.

You will gain exposure to datasets and systems such as Bloomberg PORT, Charles River, Eagle, and Snowflake while developing practical skills in data management.